WSDM2022 Challenge - Large scale temporal graph link prediction

Overview

WSDM 2022 Large-scale Temporal Graph Link Prediction - Baseline and Initial Test Set

WSDM Cup Website link

Link to this challenge

This branch offers

  • An initial test set having a small number of test examples for each dataset, together with their labels in exist column. Note that this test set only serves for development purposes. So
    • The intermediate and final dataset will not contain the exist column.
    • This is not the intermediate dataset we will be using for ranking solutions.
  • A simple baseline that trains on both datasets.

Download links to initial test set: Dataset A Dataset B

Baseline description

The baseline is only a minimal working example for both datasets, and it is certainly not optimal. You are encouraged to tweak it or propose your own solutions from scratch!

Here we summarize our baseline: The baseline is an RGCN-like GNN model trained on the entire graph. Event timestamps on the graph are encoded by decomposing the 10-digit decimal integers into 10-dimensional vectors, each element representing a digit. We train the model as binary classification using a negative-sampling-like strategy. Given a ground truth event (s, d, r, t) with source node s, destination node d, event type r and timestamp t, we perturb t to obtain a new value t'. We label the quadruplet with 1 if the new timestamp is larger than the original timestamp, and 0 otherwise. The model is essentially trained to predict p(t < t' | s, d, r), i.e. the probability that an edge with type r exists from source s and destination d before timestamp t'.

Baseline usage

To use the baseline you need to install DGL.

You also need at least 64GB of CPU memory. GPU is not required.

  1. Convert csv file to DGL graph objects.

    python csv2DGLgraph.py --dataset [A or B]
    
  2. Training.

    python base_pipeline.py --dataset [A or B]
    

Performance on Initial Test Set

The baseline got AUC of 0.511 on Dataset A and 0.510 on Dataset B.

Owner
Deep Graph Library
Deep Graph Library
Pixel Consensus Voting for Panoptic Segmentation (CVPR 2020)

Implementation for Pixel Consensus Voting (CVPR 2020). This codebase contains the essential ingredients of PCV, including various spatial discretizati

Haochen 23 Oct 25, 2022
Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Residual Dense Network for Image Super-Resolution This repository is for RDN introduced in the following paper Yulun Zhang, Yapeng Tian, Yu Kong, Bine

Yulun Zhang 494 Dec 30, 2022
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

20 Oct 24, 2022
Generating Fractals on Starknet with Cairo

StarknetFractals Generating the mandelbrot set on Starknet Current Implementation generates 1 pixel of the fractal per call(). It takes a few minutes

Orland0x 10 Jul 16, 2022
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation

DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation This project hosts the code for implementing the DCT-MASK algorithms

Alibaba Cloud 57 Nov 27, 2022
This repo is duplication of jwyang/faster-rcnn.pytorch

Faster RCNN Pytorch This repo is duplication of jwyang/faster-rcnn.pytorch C/C++ code are removed and easier to study. Python 3.8.5 Ubuntu 20.04.1 LTS

Kim Jihwan 1 Jan 14, 2022
The `rtdl` library + The official implementation of the paper

The `rtdl` library + The official implementation of the paper "Revisiting Deep Learning Models for Tabular Data"

Yandex Research 510 Dec 30, 2022
Homepage of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [Official Paddle Implementation] [Huggingface Gradio Demo] [Unofficial

442 Dec 16, 2022
ColossalAI-Examples - Examples of training models with hybrid parallelism using ColossalAI

ColossalAI-Examples This repository contains examples of training models with Co

HPC-AI Tech 185 Jan 09, 2023
WaveFake: A Data Set to Facilitate Audio DeepFake Detection

WaveFake: A Data Set to Facilitate Audio DeepFake Detection This is the code repository for our NeurIPS 2021 (Track on Datasets and Benchmarks) paper

Chair for Sys­tems Se­cu­ri­ty 27 Dec 22, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The

FengZhang 34 Dec 04, 2022
Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Style Transformer for Image Inversion and Editing (CVPR2022) https://arxiv.org/abs/2203.07932 Existing GAN inversion methods fail to provide latent co

Xueqi Hu 153 Dec 02, 2022
Punctuation Restoration using Transformer Models for High-and Low-Resource Languages

Punctuation Restoration using Transformer Models This repository contins official implementation of the paper Punctuation Restoration using Transforme

Tanvirul Alam 142 Jan 01, 2023
Continuous Diffusion Graph Neural Network

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Twitter Research 227 Jan 05, 2023
Motion Reconstruction Code and Data for Skills from Videos (SFV)

Motion Reconstruction Code and Data for Skills from Videos (SFV) This repo contains the data and the code for motion reconstruction component of the S

268 Dec 01, 2022
Catbird is an open source paraphrase generation toolkit based on PyTorch.

Catbird is an open source paraphrase generation toolkit based on PyTorch. Quick Start Requirements and Installation The project is based on PyTorch 1.

Afonso Salgado de Sousa 5 Dec 15, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
[ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated [talk] [poster] [appendix] Yuning You, Tianlong C

Shen Lab at Texas A&M University 80 Nov 23, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
A modification of Daniel Russell's notebook merged with Katherine Crowson's hq-skip-net changes

Edits made to this repo by Katherine Crowson I have added several features to this repository for use in creating higher quality generative art (featu

Paul Fishwick 10 May 07, 2022